Key uses of advanced analytics platforms

While we have talked about advanced analytics platforms in the past, we have yet to discuss their application in data analytics operations. As mentioned before, advanced analytics can do far more than the standard analytics software because of technology like AI. This sets the groundwork for more advanced analytical methods like machine learning, sentiment analysis, and cluster analysis. 

These advanced functions are invaluable for most organisations because it expands the functionality of data collection and analysis. With this in mind, we are going to discuss the key applications for advanced analytics platforms.

Key applications for advanced analytics platforms 

Advanced data analytics has the technology to implement transformative processes for organisations. 

Here are some of the options. 

Analytics for the Agile-Lean model (also known as the Lean-Agile model)

Advanced analytics comes with several advanced features like reporting, forecasting, and process enhancement. This means analytics platforms can lay the groundwork for potentially transformative business operations, like the Agile-Lean model. 

The Agile-Lean model refers to a business-oriented model where organisations are better placed to respond to unexpected exogenous events. By using the power of predictive analysis models, organisations can create a more flexible supply chain that is better placed to respond to external events. For example, supply chain planners can create a supply model that can respond to unforeseen or complicated events while maintaining a consistent production line that delivers on time, despite the exogenous shocks. 

Optimised demand planning capabilities 

Most firms with a global supply chain have complex planning procedures and processes. For most organisations, this is a rather inefficient process. One that takes up a ton of time, drives up costs, and takes valuable time away from productive work. 

Advanced data analytics can help alleviate some of the issues that come with planning and coordination by bringing visibility to the entire supply chain. Greater visibility in the supply chain allows organisations to optimise procedures. It’s much easier to identify where procedures are going wrong and take action to rectify them.

This advanced analytics allows organisations to act more proactively when dealing with unexpected occurrences, like stormy weather.

Advanced analytics can automate decision making

While professionals are still an integral part of the decision-making process, advanced analytics is reducing the burden that professionals normally have to deal with. The analytics platform comes with several features that include artificial intelligence, which allows organisations to automate part of their processes. 

One example is inventory management. The process of replenishing certain parts or components is usually a manual process. A process that consumes time and resources. Advanced data analytics can automate most repetitive functions like inventory analysis. This allows organisations to cut operating costs and reallocate resources to more pivotal areas. 

Bring new products and services to market

The process of bringing a new product or service to the market is not an easy one due to external and internal factors. Organisations need a thorough understanding of the market. They need to know what their consumers want, what their pain points are, and how they are looking to deal with their problems. 

Similarly, organisations need to understand their competition, what they are doing, and how they are reaching out to their customers. This leads to a complex business process that involves marketing and research. 

Advanced analytics platforms allow organisations to make this process far more efficient than before. Data analytics helps organisations access real-time data, which gives organisations a better understanding of their supply chain. Furthermore, with the use of machine learning, organisations can learn the market and even predict where the industry is heading—making it easier to anticipate market trends and act accordingly. 

It is also important to note that machine learning can be used to optimise company functions, eliminating inefficiencies, reducing operating costs, and bringing products to the market faster. 

Using advanced analytics to maximise operational efficiency

Advanced analytics have a potentially transformative impact on organisations. Most companies are struggling to deal with an assortment of problems ranging from complex supply chains to inefficient, bureaucratic red tape. 

Data analytics platforms can provide the insight most organisations need to fix the underlying problems in their processes. By fixing these problems, organisations can attain new heights in operational efficiency, allowing them to reduce costs and bring innovations to the market at a faster rate. These are the key applications of advanced analytics. 

Visit Selerity to know more about advanced analytics platforms.

Marketing data analytics is fueling the data-driven marketer

Big data is the biggest game-changing opportunity for marketing and sales since the Internet went mainstream almost 25 years ago. The data big bang has unleashed terabytes of information about everything from customer behaviours to weather patterns to demographic consumer shifts in emerging markets. This goldmine of data represents a pivot-point moment for marketing and sales leaders. Companies that inject marketing data analytics into their operation show productivity rates and profitability that are 5% to 6% higher than those of their peers. That’s an advantage no company can afford to ignore.

With the market becoming more complex and customers demanding a personalised experience, data-driven marketing is the only way forward. As we head into 2019, marketers are more focused than ever on using data-driven insights and marketing data analytics to better understand their customers and boost their overall competitive advantage.

More than 63% of marketers reported they have increased their spending on data-driven marketing, and around 20% of all marketing spend goes on data-driven advertising campaigns. Moreover, companies who adopt data-driven marketing are more likely to have an advantage over the competition and increase profitability. In fact- they are six times more likely to be profitable year-over-year.

Read on to see how marketing data analytics has given business leaders significant decision-making firepower.

Big data marketing data analytics = Big opportunities for targeted advertising

Marketers are collecting the data produced from a variety of live customer touch-points to paint a complete picture of each customer’s behaviour. Analysing this large amount of data in motion enables marketers to fine-tune customer segmentation models and apply the insights to develop customer engagement strategies that improve the value of customer interactions.

As the number of customer channels increases, marketers need to ensure that they are delivering a tailored experience across all channels. All of these efforts help provide a highly personalised experience while maximising the return on the marketing investment. In the long-run, marketers can feed these new, real-time insights back into the organisation to influence product development and product pricing as well.

Marketers are able to plan and forecast much better with big data

As Werner Vogels, Amazon’s chief technology officer, said “You can never have too much data. Bigger is definitely better. The more data you can collect, the finer-grained the results can be.”

Data scientists provide marketing departments with a superb analysis of the latest trends in customer behaviour, which allows marketers to create comprehensive strategies and prepare for more efficient activities.

That’s why almost 65% of marketing executives claim that data-driven marketing is crucial to success in a hypercompetitive global economy. They are now able to target consumers not only as large groups but also as segmented sub-groups with their own specific features, which gives them the possibility to modify activities and adapt to each one of these audiences individually.

Moreover, data science is capable of analysing current marketing strategies but it also has the ability to successfully predict future trends. That’s why marketers utilise it to create business forecasts, which allows them to behave proactively and go one step ahead of the competitors. In an environment where there is a constant struggle and striving for more market share, big data turns out to be essential for many companies.

Machine-powered analytics paving the way for the future

I’m not saying that artificial intelligence and big data will spell out the death of the human analyst, but it’s certainly true that if marketers want to draw conclusions from a huge pool of data, they’ll need the support of a machine to help to process it.

Because of this, the digital marketers of the future will need to work in tandem with machines, devices, and equipment to analyse data and make decisions based on their insights. No matter how much technology evolves, there will always be a need for some degree of human overwatch – and that’s even truer when it comes to the complicated field of big data analytics. No human can, nor should, go at it alone, and neither can any single piece of software. The combination of the two will be far more powerful than just the sum of their parts.

Key takeaways

Sophisticated analytics solutions for big data provide new approaches to addressing some of the key marketing imperatives while delivering impressive results. These solutions can transform traditional marketing roles and improve how to execute essential marketing functions.

For more information on this data-driven industry, visit check out our services.